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Refactor tests with data generator. (#5439)

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This commit is contained in:
Jiaming Yuan
2020-03-27 06:44:44 +08:00
committed by GitHub
parent 7146b91d5a
commit 4942da64ae
26 changed files with 334 additions and 259 deletions
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14
tests/cpp/predictor/test_cpu_predictor.cc
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@@ -24,11 +24,11 @@ TEST(CpuPredictor, Basic) {
gbm::GBTreeModel model = CreateTestModel(&param);
auto dmat = CreateDMatrix(kRows, kCols, 0);
auto dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatix();
// Test predict batch
PredictionCacheEntry out_predictions;
cpu_predictor->PredictBatch((*dmat).get(), &out_predictions, model, 0);
cpu_predictor->PredictBatch(dmat.get(), &out_predictions, model, 0);
ASSERT_EQ(model.trees.size(), out_predictions.version);
std::vector<float>& out_predictions_h = out_predictions.predictions.HostVector();
for (size_t i = 0; i < out_predictions.predictions.Size(); i++) {
@@ -36,7 +36,7 @@ TEST(CpuPredictor, Basic) {
}
// Test predict instance
auto &batch = *(*dmat)->GetBatches<xgboost::SparsePage>().begin();
auto const &batch = *dmat->GetBatches<xgboost::SparsePage>().begin();
for (size_t i = 0; i < batch.Size(); i++) {
std::vector<float> instance_out_predictions;
cpu_predictor->PredictInstance(batch[i], &instance_out_predictions, model);
@@ -45,14 +45,14 @@ TEST(CpuPredictor, Basic) {
// Test predict leaf
std::vector<float> leaf_out_predictions;
cpu_predictor->PredictLeaf((*dmat).get(), &leaf_out_predictions, model);
cpu_predictor->PredictLeaf(dmat.get(), &leaf_out_predictions, model);
for (auto v : leaf_out_predictions) {
ASSERT_EQ(v, 0);
}
// Test predict contribution
std::vector<float> out_contribution;
cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model);
cpu_predictor->PredictContribution(dmat.get(), &out_contribution, model);
ASSERT_EQ(out_contribution.size(), kRows * (kCols + 1));
for (size_t i = 0; i < out_contribution.size(); ++i) {
auto const& contri = out_contribution[i];
@@ -64,7 +64,7 @@ TEST(CpuPredictor, Basic) {
}
}
// Test predict contribution (approximate method)
cpu_predictor->PredictContribution((*dmat).get(), &out_contribution, model, 0, nullptr, true);
cpu_predictor->PredictContribution(dmat.get(), &out_contribution, model, 0, nullptr, true);
for (size_t i = 0; i < out_contribution.size(); ++i) {
auto const& contri = out_contribution[i];
// shift 1 for bias, as test tree is a decision dump, only global bias is filled with LeafValue().
@@ -74,8 +74,6 @@ TEST(CpuPredictor, Basic) {
ASSERT_EQ(contri, 0);
}
}
delete dmat;
}
TEST(CpuPredictor, ExternalMemory) {